Enhancing Task-Specific Adaptation for Video Foundation Models: Introducing Video Adapter as a Probabilistic Framework for Adapting Text-to-Video Models

This method allows high quality and customized video synthesis with a small model and limited data

Large text-to-video models trained on internet-scale data have shown extraordinary capabilities to generate high-fidelity films from arbitrarily written descriptions. However, fine-tuning a pretrained huge model might be prohibitively expensive, making it difficult to adapt these models to applications with limited domain-specific data, such as animation or robotics videos. Researchers from Google DeepMind, UC Berkeley, MIT and the University of Alberta look into how a large pretrained text-to-video model can be customized to a variety of downstream domains and tasks without fine-tuning, inspired by how a small modifiable component (such as prompts, prefix-tuning) can enable a large language model to perform new tasks without requiring access to the model weights. To address this, they present Video Adapter, a method for generating task-specific tiny video models by using a large pretrained video diffusion model’s score function as a prior probabilistic. Experiments demonstrate that Video Adapters can use as few as 1.25 percent of the pretrained model’s parameters to include the wide knowledge and maintain the high fidelity of a big pretrained video model in a task-specific tiny video model. High-quality, task-specific movies can be generated using Video Adapters for various uses, including but not limited to animation, egocentric modeling, and the modeling of simulated and real-world robotics data.

Researchers test Video Adapter on various video creation jobs. On the difficult Ego4D data and the robotic Bridge data, Video Adapter generates videos with better FVD and Inception Scores than a high-quality pretrained big video model while using up to 80x fewer parameters. Researchers demonstrate qualitatively that Video Adapter permits the production of genre-specific videos like those found in science fiction and animation. In addition, the study’s authors show how Video Adapter can pave the way for bridging robotics’ infamous sim-to-real gap by modeling both real and simulated robotic films and allowing data augmentation on actual robotic videos via individualized stylization.

Key Features

  • To achieve high-quality yet versatile video synthesis without requiring gradient updates on the pretrained model, Video Adapter combines the scores of a pretrained text-to-video model with the scores of a domain-specific tiny model (with 1% parameters) at sampling time.
  • Pretrained video models can be easily adapted using Video Adapter to movies of humans and robotic data.
  • Under the same number of TPU hours, Video Adapter gets higher FVD, FID, and Inception Scores than the pretrained and task-specific models.
  • Potential uses for video adapters range from use in anime production to domain randomization to bridge the simulation-reality gap in robotics.
  • As opposed to a huge video model pretrained from internet data, Video Adapter requires training a tiny domain-specific text-to-video model with orders of magnitude fewer parameters. Video Adapter achieves high-quality and adaptable video synthesis by composing the pretrained and domain-specific video model scores during sampling.
  • With Video Adapter, you may give a video a unique look using a model only exposed to one type of animation.
  • Using a Video Adapter, a pretrained model of considerable size can take on the visual characteristics of an animation model of a much smaller size.
  • With the help of a Video Adapter, a massive pre-trained model can take on the visual aesthetic of a diminutive Sci-Fi animation model.
  • Video Adapters may generate various movies in various genres and styles, including videos with egocentric motions based on manipulation and navigation, videos with individualized genres like animation and science fiction, and videos with simulated and genuine robotic motions.

Limitations

A small video model still needs to be trained on domain-specific data; therefore, while Video Adapter can effectively adapt big pretrained text-to-video models, it is not training-free. Another difference between Video Adapter and other text-to-image and text-to-video APIs is that it requires the score to be output alongside the resulting video. Video Adapter effectively makes text-to-video research more accessible to small industrial and academic institutions by addressing the lack of free access to model weights and computing efficiency.

To sum it up

It is obvious that when text-to-video foundation models expand in size, they will need to be effectively adapted to task-specific usage. Researchers have developed Video Adapter, a powerful method for generating domain and task-specific films by employing huge pretrained text-to-video models as a probabilistic prior. Video Adapters may synthesize high-quality videos in specialized disciplines or desired aesthetics without requiring more fine-tuning of the massive pretrained model.


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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.

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